Title: Automatic Stage Lighting Control: Is it a Rule-Driven Process or Generative Task? Show Chinese title

Title: 自动舞台灯光控制：是规则驱动的过程还是生成任务？

Abstract: Stage lighting plays an essential role in live music performances, influencing the engaging experience of both musicians and audiences. Given the high costs associated with hiring or training professional lighting engineers, Automatic Stage Lighting Control (ASLC) has gained increasing attention. However, most existing approaches only classify music into limited categories and map them to predefined light patterns, resulting in formulaic and monotonous outcomes that lack rationality. To address this issue, this paper presents an end-to-end solution that directly learns from experienced lighting engineers -- Skip-BART. To the best of our knowledge, this is the first work to conceptualize ASLC as a generative task rather than merely a classification problem. Our method modifies the BART model to take audio music as input and produce light hue and value (intensity) as output, incorporating a novel skip connection mechanism to enhance the relationship between music and light within the frame grid.We validate our method through both quantitative analysis and an human evaluation, demonstrating that Skip-BART outperforms conventional rule-based methods across all evaluation metrics and shows only a limited gap compared to real lighting engineers.Specifically, our method yields a p-value of 0.72 in a statistical comparison based on human evaluations with human lighting engineers, suggesting that the proposed approach closely matches human lighting engineering performance. To support further research, we have made our self-collected dataset, code, and trained model parameters available at https://github.com/RS2002/Skip-BART . Show Chinese abstract

Abstract: 舞台灯光在现场音乐表演中扮演着至关重要的角色，影响着音乐家和观众的参与体验。 鉴于聘请或培训专业灯光工程师的高昂成本， 自动舞台灯光控制（ASLC）引起了越来越多的关注。 然而，大多数现有的方法仅将音乐分类为有限的类别，并将其映射到预定义的灯光模式上，导致结果公式化且单调乏味，缺乏合理性。 为了解决这个问题，本文提出了一种端到端的解决方案，直接从有经验的灯光工程师那里学习——Skip-BART。 据我们所知，这是首次将ASLC概念化为生成任务而非仅仅分类问题的工作。 我们的方法修改了BART模型，使其以音频音乐作为输入，输出灯光色调和值（强度），并在网格框架内引入了一种新颖的跳过连接机制，以增强音乐与灯光之间的关系。我们通过定量分析和人工评估验证了我们的方法，结果显示Skip-BART在所有评估指标上都优于传统的基于规则的方法，并且与实际灯光工程师的表现差距有限。具体来说，在基于人工评估与真人灯光工程师的统计比较中，我们的方法获得了0.72的p值，表明所提出的方案接近人类灯光工程表现。 为了支持进一步的研究，我们将自收集的数据集、代码和训练好的模型参数发布在了https://github.com/RS2002/Skip-BART 。